While machine learning approaches to visual emotion recognition offer great
promise, current methods consider training and testing models on small scale
datasets covering limited visual emotion concepts. Our analysis identifies an
important but long overlooked issue of existing visual emotion benchmarks in
the form of dataset biases. We design a series of tests to show and measure how
such dataset biases obstruct learning a generalizable emotion recognition
model. Based on our analysis, we propose a webly supervised approach by
leveraging a large quantity of stock image data. Our approach uses a simple yet
effective curriculum guided training strategy for learning discriminative
emotion features. We discover that the models learned using our large scale
stock image dataset exhibit significantly better generalization ability than
the existing datasets without the manual collection of even a single label.
Moreover, visual representation learned using our approach holds a lot of
promise across a variety of tasks on different image and video datasets.

机器学习在视觉情感识别方面的应用具有很大的潜力，但目前的方法针对有限的视觉情感概念局限于小规模数据集上进行模型训练和测试。我们的分析确定了现有视觉情感基准测试中存在的一个长期被忽视的问题，即数据集偏见。基于我们的分析，我们提出了一个基于 Webly 监督方法的解决方案，通过利用大量的库存图像数据进行训练。我们发现，使用我们的大规模图像数据集学习的模型表现出了明显更好的泛化能力。此外，使用我们的方法学习得到的视觉表示在不同的图像和视频数据集上具有很大的潜力。